Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer

Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower t...

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Published inNature communications Vol. 15; no. 1; pp. 6916 - 13
Main Authors Byrne, Ashley, Le, Daniel, Sereti, Kostianna, Menon, Hari, Vaidya, Samir, Patel, Neha, Lund, Jessica, Xavier-Magalhães, Ana, Shi, Minyi, Liang, Yuxin, Sterne-Weiler, Timothy, Modrusan, Zora, Stephenson, William
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 12.08.2024
Nature Publishing Group
Nature Portfolio
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ISSN2041-1723
2041-1723
DOI10.1038/s41467-024-51252-6

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Abstract Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution. Long-read sequencing allows the detection of RNA isoforms, but is hampered by low throughput and potential artefacts. Here, the authors develop the scTaILoR-seq hybridisation capture method for long-read RNA sequencing to improve transcript detection, and use this method to detect isoforms at the single-cell level in ovarian cancer.
AbstractList Abstract Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.
Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.Long-read sequencing allows the detection of RNA isoforms, but is hampered by low throughput and potential artefacts. Here, the authors develop the scTaILoR-seq hybridisation capture method for long-read RNA sequencing to improve transcript detection, and use this method to detect isoforms at the single-cell level in ovarian cancer.
Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution. Long-read sequencing allows the detection of RNA isoforms, but is hampered by low throughput and potential artefacts. Here, the authors develop the scTaILoR-seq hybridisation capture method for long-read RNA sequencing to improve transcript detection, and use this method to detect isoforms at the single-cell level in ovarian cancer.
Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.
Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.
ArticleNumber 6916
Author Byrne, Ashley
Modrusan, Zora
Sereti, Kostianna
Shi, Minyi
Menon, Hari
Patel, Neha
Vaidya, Samir
Le, Daniel
Xavier-Magalhães, Ana
Sterne-Weiler, Timothy
Liang, Yuxin
Stephenson, William
Lund, Jessica
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Snippet Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for...
Abstract Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for...
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SubjectTerms 13/62
38/91
45/41
631/1647/2217/2018
631/61/212/2019
631/67/1517/1709
631/67/69
Allelic Imbalance - genetics
Cancer
Cell Line, Tumor
Female
Gene Expression Profiling - methods
Gene Expression Regulation, Neoplastic
Gene sequencing
High-Throughput Nucleotide Sequencing - methods
Humanities and Social Sciences
Humans
Hybridization
Isoforms
multidisciplinary
Nucleotides
Ovarian cancer
Ovarian Neoplasms - genetics
Polymorphism, Single Nucleotide
Ribonucleic acid
RNA
RNA Isoforms - genetics
Science
Science (multidisciplinary)
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Transcriptome - genetics
Transcriptomes
Tumor cell lines
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Title Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer
URI https://link.springer.com/article/10.1038/s41467-024-51252-6
https://www.ncbi.nlm.nih.gov/pubmed/39134520
https://www.proquest.com/docview/3092134141
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https://pubmed.ncbi.nlm.nih.gov/PMC11319652
https://doaj.org/article/ea2115afd9f64a1e84e08c68a787dbc9
Volume 15
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